Relationships between nuclei within gland epithelia have diagnostic importance. For example, one marker of adenocarcinomas is a loss of epithelial polarity. Loss of polarity may be described roughly as the lack of a parallel relationship between long axes of neighboring nuclei (approximated by ellipsoids). As another example, the appearance of multilayer nuclear organization in an epithelium suggests malignancy.
The prior art has addressed the problem of distinguishing portions of tissue according to their type (epithelium, stroma, etc.) in a couple of methods. The reference entitled “Image Processing And Neural Networks For Early Detection Of Histological Changes,” by J. Ramirex-Nino et al., describes a linear classifier (trained as a linear neural network) is used for classifying each pixel of the scanned image into one of four tissue categories according to its color. Then hand-designed heuristics are used to find the boundaries of the epithelium. The reference entitled, “Multifeature Prostate Cancer Diagnosis And Gleason Grading Of Histological Images,” by A. Tabesh et al., describe applying a color segmentation to the tissue image and classifying each image segment as one of several different objects according to hand-designed heuristics based on its color, some basic shape features, and the earlier classification of nearby segments. One of the classes available is epithelial nuclei, which are defined as segments that have a hematoxylin color and are neither round enough to look like stromal nuclei nor large and dark enough to look like apoptotic nuclei.
Although the prior art has addressed the problem of distinguishing portions of tissue according to their type, there remains a need for an improved method for detecting epithelial structures.